Overview

Dataset statistics

Number of variables41
Number of observations801
Missing cells522
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory256.7 KiB
Average record size in memory328.2 B

Variable types

Categorical21
Numeric20

Warnings

abilities has a high cardinality: 482 distinct values High cardinality
classfication has a high cardinality: 588 distinct values High cardinality
japanese_name has a high cardinality: 801 distinct values High cardinality
name has a high cardinality: 801 distinct values High cardinality
pokedex_number is highly correlated with generationHigh correlation
generation is highly correlated with pokedex_numberHigh correlation
height_m has 20 (2.5%) missing values Missing
percentage_male has 98 (12.2%) missing values Missing
type2 has 384 (47.9%) missing values Missing
weight_kg has 20 (2.5%) missing values Missing
classfication is uniformly distributed Uniform
japanese_name is uniformly distributed Uniform
name is uniformly distributed Uniform
pokedex_number is uniformly distributed Uniform
japanese_name has unique values Unique
name has unique values Unique
pokedex_number has unique values Unique
against_electric has 64 (8.0%) zeros Zeros
against_fight has 41 (5.1%) zeros Zeros
against_ground has 98 (12.2%) zeros Zeros
against_poison has 46 (5.7%) zeros Zeros
against_psychic has 46 (5.7%) zeros Zeros
base_happiness has 36 (4.5%) zeros Zeros
percentage_male has 27 (3.4%) zeros Zeros

Reproduction

Analysis started2022-01-06 14:31:09.048194
Analysis finished2022-01-06 14:32:27.209683
Duration1 minute and 18.16 seconds
Software versionpandas-profiling v2.13.0
Download configurationconfig.yaml

Variables

abilities
Categorical

HIGH CARDINALITY

Distinct482
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
['Levitate']
 
29
['Beast Boost']
 
7
['Shed Skin']
 
5
['Justified']
 
4
['Poison Point', 'Rivalry', 'Hustle']
 
4
Other values (477)
752 

Length

Max length89
Median length35
Mean length32.37827715
Min length9

Characters and Unicode

Total characters25935
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique261 ?
Unique (%)32.6%

Sample

1st row['Overgrow', 'Chlorophyll']
2nd row['Overgrow', 'Chlorophyll']
3rd row['Overgrow', 'Chlorophyll']
4th row['Blaze', 'Solar Power']
5th row['Blaze', 'Solar Power']
ValueCountFrequency (%)
['Levitate']29
 
3.6%
['Beast Boost']7
 
0.9%
['Shed Skin']5
 
0.6%
['Justified']4
 
0.5%
['Poison Point', 'Rivalry', 'Hustle']4
 
0.5%
['Keen Eye', 'Tangled Feet', 'Big Pecks']4
 
0.5%
['Clear Body', 'Light Metal']4
 
0.5%
['Torrent', 'Sheer Force']3
 
0.4%
['Blaze', 'Flash Fire']3
 
0.4%
['Overgrow', 'Leaf Guard']3
 
0.4%
Other values (472)735
91.8%
2022-01-06T15:32:27.478039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
armor53
 
1.8%
sand51
 
1.7%
body51
 
1.7%
veil48
 
1.6%
guard47
 
1.6%
sturdy41
 
1.4%
force41
 
1.4%
swift38
 
1.3%
swim38
 
1.3%
water38
 
1.3%
Other values (286)2526
85.0%

Most occurring characters

ValueCountFrequency (%)
'3972
 
15.3%
2171
 
8.4%
e1716
 
6.6%
r1386
 
5.3%
,1185
 
4.6%
a1133
 
4.4%
t1112
 
4.3%
i1101
 
4.2%
o1095
 
4.2%
n976
 
3.8%
Other values (46)10088
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14036
54.1%
Other Punctuation5157
 
19.9%
Uppercase Letter2968
 
11.4%
Space Separator2171
 
8.4%
Open Punctuation801
 
3.1%
Close Punctuation801
 
3.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e1716
12.2%
r1386
 
9.9%
a1133
 
8.1%
t1112
 
7.9%
i1101
 
7.8%
o1095
 
7.8%
n976
 
7.0%
l753
 
5.4%
u579
 
4.1%
d512
 
3.6%
Other values (16)3673
26.2%
ValueCountFrequency (%)
S599
20.2%
F227
 
7.6%
C178
 
6.0%
P176
 
5.9%
B173
 
5.8%
A163
 
5.5%
T156
 
5.3%
R148
 
5.0%
I142
 
4.8%
L126
 
4.2%
Other values (14)880
29.6%
ValueCountFrequency (%)
'3972
77.0%
,1185
 
23.0%
ValueCountFrequency (%)
[801
100.0%
ValueCountFrequency (%)
2171
100.0%
ValueCountFrequency (%)
]801
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17004
65.6%
Common8931
34.4%

Most frequent character per script

ValueCountFrequency (%)
e1716
 
10.1%
r1386
 
8.2%
a1133
 
6.7%
t1112
 
6.5%
i1101
 
6.5%
o1095
 
6.4%
n976
 
5.7%
l753
 
4.4%
S599
 
3.5%
u579
 
3.4%
Other values (40)6554
38.5%
ValueCountFrequency (%)
'3972
44.5%
2171
24.3%
,1185
 
13.3%
[801
 
9.0%
]801
 
9.0%
-1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25935
100.0%

Most frequent character per block

ValueCountFrequency (%)
'3972
 
15.3%
2171
 
8.4%
e1716
 
6.6%
r1386
 
5.3%
,1185
 
4.6%
a1133
 
4.4%
t1112
 
4.3%
i1101
 
4.2%
o1095
 
4.2%
n976
 
3.8%
Other values (46)10088
38.9%

against_bug
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
376 
0.5
247 
2.0
128 
0.25
42 
4.0
 
8

Length

Max length4
Median length3
Mean length3.052434457
Min length3

Characters and Unicode

Total characters2445
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.5
5th row0.5
ValueCountFrequency (%)
1.0376
46.9%
0.5247
30.8%
2.0128
 
16.0%
0.2542
 
5.2%
4.08
 
1.0%
2022-01-06T15:32:27.745332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:27.828650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0376
46.9%
0.5247
30.8%
2.0128
 
16.0%
0.2542
 
5.2%
4.08
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.801
32.8%
0801
32.8%
1376
15.4%
5289
 
11.8%
2170
 
7.0%
48
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1644
67.2%
Other Punctuation801
32.8%

Most frequent character per category

ValueCountFrequency (%)
0801
48.7%
1376
22.9%
5289
 
17.6%
2170
 
10.3%
48
 
0.5%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2445
100.0%

Most frequent character per script

ValueCountFrequency (%)
.801
32.8%
0801
32.8%
1376
15.4%
5289
 
11.8%
2170
 
7.0%
48
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2445
100.0%

Most frequent character per block

ValueCountFrequency (%)
.801
32.8%
0801
32.8%
1376
15.4%
5289
 
11.8%
2170
 
7.0%
48
 
0.3%

against_dark
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
565 
0.5
126 
2.0
105 
0.25
 
3
4.0
 
2

Length

Max length4
Median length3
Mean length3.003745318
Min length3

Characters and Unicode

Total characters2406
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0565
70.5%
0.5126
 
15.7%
2.0105
 
13.1%
0.253
 
0.4%
4.02
 
0.2%
2022-01-06T15:32:28.050195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:28.136496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0565
70.5%
0.5126
 
15.7%
2.0105
 
13.1%
0.253
 
0.4%
4.02
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.801
33.3%
0801
33.3%
1565
23.5%
5129
 
5.4%
2108
 
4.5%
42
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1605
66.7%
Other Punctuation801
33.3%

Most frequent character per category

ValueCountFrequency (%)
0801
49.9%
1565
35.2%
5129
 
8.0%
2108
 
6.7%
42
 
0.1%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2406
100.0%

Most frequent character per script

ValueCountFrequency (%)
.801
33.3%
0801
33.3%
1565
23.5%
5129
 
5.4%
2108
 
4.5%
42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2406
100.0%

Most frequent character per block

ValueCountFrequency (%)
.801
33.3%
0801
33.3%
1565
23.5%
5129
 
5.4%
2108
 
4.5%
42
 
0.1%

against_dragon
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
669 
0.0
 
47
2.0
 
43
0.5
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2403
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0669
83.5%
0.047
 
5.9%
2.043
 
5.4%
0.542
 
5.2%
2022-01-06T15:32:28.348881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:28.416046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0669
83.5%
0.047
 
5.9%
2.043
 
5.4%
0.542
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0848
35.3%
.801
33.3%
1669
27.8%
243
 
1.8%
542
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1602
66.7%
Other Punctuation801
33.3%

Most frequent character per category

ValueCountFrequency (%)
0848
52.9%
1669
41.8%
243
 
2.7%
542
 
2.6%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2403
100.0%

Most frequent character per script

ValueCountFrequency (%)
0848
35.3%
.801
33.3%
1669
27.8%
243
 
1.8%
542
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2403
100.0%

Most frequent character per block

ValueCountFrequency (%)
0848
35.3%
.801
33.3%
1669
27.8%
243
 
1.8%
542
 
1.7%

against_electric
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.073970037
Minimum0
Maximum4
Zeros64
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:28.497690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.6549616165
Coefficient of variation (CV)0.609850921
Kurtosis2.113157
Mean1.073970037
Median Absolute Deviation (MAD)0.5
Skewness0.9348396916
Sum860.25
Variance0.4289747191
MonotonicityNot monotonic
2022-01-06T15:32:28.608874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1392
48.9%
2181
22.6%
0.5156
 
19.5%
064
 
8.0%
47
 
0.9%
0.251
 
0.1%
ValueCountFrequency (%)
064
 
8.0%
0.251
 
0.1%
0.5156
 
19.5%
1392
48.9%
2181
22.6%
ValueCountFrequency (%)
47
 
0.9%
2181
22.6%
1392
48.9%
0.5156
 
19.5%
0.251
 
0.1%

against_fairy
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
541 
0.5
145 
2.0
103 
4.0
 
9
0.25
 
3

Length

Max length4
Median length3
Mean length3.003745318
Min length3

Characters and Unicode

Total characters2406
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5
ValueCountFrequency (%)
1.0541
67.5%
0.5145
 
18.1%
2.0103
 
12.9%
4.09
 
1.1%
0.253
 
0.4%
2022-01-06T15:32:28.891258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:28.969940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0541
67.5%
0.5145
 
18.1%
2.0103
 
12.9%
4.09
 
1.1%
0.253
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0801
33.3%
.801
33.3%
1541
22.5%
5148
 
6.2%
2106
 
4.4%
49
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1605
66.7%
Other Punctuation801
33.3%

Most frequent character per category

ValueCountFrequency (%)
0801
49.9%
1541
33.7%
5148
 
9.2%
2106
 
6.6%
49
 
0.6%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2406
100.0%

Most frequent character per script

ValueCountFrequency (%)
0801
33.3%
.801
33.3%
1541
22.5%
5148
 
6.2%
2106
 
4.4%
49
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2406
100.0%

Most frequent character per block

ValueCountFrequency (%)
0801
33.3%
.801
33.3%
1541
22.5%
5148
 
6.2%
2106
 
4.4%
49
 
0.4%

against_fight
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.065543071
Minimum0
Maximum4
Zeros41
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:29.055463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.717250818
Coefficient of variation (CV)0.6731316992
Kurtosis2.681552535
Mean1.065543071
Median Absolute Deviation (MAD)0.5
Skewness1.257114827
Sum853.5
Variance0.514448736
MonotonicityNot monotonic
2022-01-06T15:32:29.163809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1326
40.7%
0.5193
24.1%
2184
23.0%
0.2544
 
5.5%
041
 
5.1%
413
 
1.6%
ValueCountFrequency (%)
041
 
5.1%
0.2544
 
5.5%
0.5193
24.1%
1326
40.7%
2184
23.0%
ValueCountFrequency (%)
413
 
1.6%
2184
23.0%
1326
40.7%
0.5193
24.1%
0.2544
 
5.5%

against_fire
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
354 
0.5
226 
2.0
187 
0.25
 
18
4.0
 
16

Length

Max length4
Median length3
Mean length3.02247191
Min length3

Characters and Unicode

Total characters2421
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row0.5
5th row0.5
ValueCountFrequency (%)
1.0354
44.2%
0.5226
28.2%
2.0187
23.3%
0.2518
 
2.2%
4.016
 
2.0%
2022-01-06T15:32:29.420454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:29.500069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0354
44.2%
0.5226
28.2%
2.0187
23.3%
0.2518
 
2.2%
4.016
 
2.0%

Most occurring characters

ValueCountFrequency (%)
.801
33.1%
0801
33.1%
1354
14.6%
5244
 
10.1%
2205
 
8.5%
416
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
66.9%
Other Punctuation801
33.1%

Most frequent character per category

ValueCountFrequency (%)
0801
49.4%
1354
21.9%
5244
 
15.1%
2205
 
12.7%
416
 
1.0%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2421
100.0%

Most frequent character per script

ValueCountFrequency (%)
.801
33.1%
0801
33.1%
1354
14.6%
5244
 
10.1%
2205
 
8.5%
416
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2421
100.0%

Most frequent character per block

ValueCountFrequency (%)
.801
33.1%
0801
33.1%
1354
14.6%
5244
 
10.1%
2205
 
8.5%
416
 
0.7%

against_flying
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
488 
2.0
181 
0.5
110 
4.0
 
12
0.25
 
10

Length

Max length4
Median length3
Mean length3.012484395
Min length3

Characters and Unicode

Total characters2413
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0488
60.9%
2.0181
 
22.6%
0.5110
 
13.7%
4.012
 
1.5%
0.2510
 
1.2%
2022-01-06T15:32:29.715994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:29.798424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0488
60.9%
2.0181
 
22.6%
0.5110
 
13.7%
4.012
 
1.5%
0.2510
 
1.2%

Most occurring characters

ValueCountFrequency (%)
.801
33.2%
0801
33.2%
1488
20.2%
2191
 
7.9%
5120
 
5.0%
412
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1612
66.8%
Other Punctuation801
33.2%

Most frequent character per category

ValueCountFrequency (%)
0801
49.7%
1488
30.3%
2191
 
11.8%
5120
 
7.4%
412
 
0.7%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2413
100.0%

Most frequent character per script

ValueCountFrequency (%)
.801
33.2%
0801
33.2%
1488
20.2%
2191
 
7.9%
5120
 
5.0%
412
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2413
100.0%

Most frequent character per block

ValueCountFrequency (%)
.801
33.2%
0801
33.2%
1488
20.2%
2191
 
7.9%
5120
 
5.0%
412
 
0.5%

against_ghost
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
536 
2.0
112 
0.0
109 
0.5
 
42
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2403
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0536
66.9%
2.0112
 
14.0%
0.0109
 
13.6%
0.542
 
5.2%
4.02
 
0.2%
2022-01-06T15:32:30.015090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:30.250846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0536
66.9%
2.0112
 
14.0%
0.0109
 
13.6%
0.542
 
5.2%
4.02
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0910
37.9%
.801
33.3%
1536
22.3%
2112
 
4.7%
542
 
1.7%
42
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1602
66.7%
Other Punctuation801
33.3%

Most frequent character per category

ValueCountFrequency (%)
0910
56.8%
1536
33.5%
2112
 
7.0%
542
 
2.6%
42
 
0.1%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2403
100.0%

Most frequent character per script

ValueCountFrequency (%)
0910
37.9%
.801
33.3%
1536
22.3%
2112
 
4.7%
542
 
1.7%
42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2403
100.0%

Most frequent character per block

ValueCountFrequency (%)
0910
37.9%
.801
33.3%
1536
22.3%
2112
 
4.7%
542
 
1.7%
42
 
0.1%

against_grass
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
297 
0.5
256 
2.0
135 
0.25
85 
4.0
 
28

Length

Max length4
Median length3
Mean length3.106117353
Min length3

Characters and Unicode

Total characters2488
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.25
2nd row0.25
3rd row0.25
4th row0.5
5th row0.5
ValueCountFrequency (%)
1.0297
37.1%
0.5256
32.0%
2.0135
16.9%
0.2585
 
10.6%
4.028
 
3.5%
2022-01-06T15:32:30.461989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:30.559674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0297
37.1%
0.5256
32.0%
2.0135
16.9%
0.2585
 
10.6%
4.028
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0801
32.2%
.801
32.2%
5341
13.7%
1297
 
11.9%
2220
 
8.8%
428
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1687
67.8%
Other Punctuation801
32.2%

Most frequent character per category

ValueCountFrequency (%)
0801
47.5%
5341
20.2%
1297
 
17.6%
2220
 
13.0%
428
 
1.7%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2488
100.0%

Most frequent character per script

ValueCountFrequency (%)
0801
32.2%
.801
32.2%
5341
13.7%
1297
 
11.9%
2220
 
8.8%
428
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2488
100.0%

Most frequent character per block

ValueCountFrequency (%)
0801
32.2%
.801
32.2%
5341
13.7%
1297
 
11.9%
2220
 
8.8%
428
 
1.1%

against_ground
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.098002497
Minimum0
Maximum4
Zeros98
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:30.661309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7388181474
Coefficient of variation (CV)0.6728747426
Kurtosis2.667510221
Mean1.098002497
Median Absolute Deviation (MAD)0
Skewness1.079249179
Sum879.5
Variance0.545852255
MonotonicityNot monotonic
2022-01-06T15:32:30.768744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1402
50.2%
2184
23.0%
098
 
12.2%
0.596
 
12.0%
415
 
1.9%
0.256
 
0.7%
ValueCountFrequency (%)
098
 
12.2%
0.256
 
0.7%
0.596
 
12.0%
1402
50.2%
2184
23.0%
ValueCountFrequency (%)
415
 
1.9%
2184
23.0%
1402
50.2%
0.596
 
12.0%
0.256
 
0.7%

against_ice
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
349 
2.0
212 
0.5
209 
4.0
 
22
0.25
 
9

Length

Max length4
Median length3
Mean length3.011235955
Min length3

Characters and Unicode

Total characters2412
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row0.5
5th row0.5
ValueCountFrequency (%)
1.0349
43.6%
2.0212
26.5%
0.5209
26.1%
4.022
 
2.7%
0.259
 
1.1%
2022-01-06T15:32:31.028851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:31.113245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0349
43.6%
2.0212
26.5%
0.5209
26.1%
4.022
 
2.7%
0.259
 
1.1%

Most occurring characters

ValueCountFrequency (%)
.801
33.2%
0801
33.2%
1349
14.5%
2221
 
9.2%
5218
 
9.0%
422
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1611
66.8%
Other Punctuation801
33.2%

Most frequent character per category

ValueCountFrequency (%)
0801
49.7%
1349
21.7%
2221
 
13.7%
5218
 
13.5%
422
 
1.4%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2412
100.0%

Most frequent character per script

ValueCountFrequency (%)
.801
33.2%
0801
33.2%
1349
14.5%
2221
 
9.2%
5218
 
9.0%
422
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2412
100.0%

Most frequent character per block

ValueCountFrequency (%)
.801
33.2%
0801
33.2%
1349
14.5%
2221
 
9.2%
5218
 
9.0%
422
 
0.9%

against_normal
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
664 
0.5
90 
0.0
 
41
0.25
 
6

Length

Max length4
Median length3
Mean length3.007490637
Min length3

Characters and Unicode

Total characters2409
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0664
82.9%
0.590
 
11.2%
0.041
 
5.1%
0.256
 
0.7%
2022-01-06T15:32:31.334382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:31.416513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0664
82.9%
0.590
 
11.2%
0.041
 
5.1%
0.256
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0842
35.0%
.801
33.3%
1664
27.6%
596
 
4.0%
26
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1608
66.7%
Other Punctuation801
33.3%

Most frequent character per category

ValueCountFrequency (%)
0842
52.4%
1664
41.3%
596
 
6.0%
26
 
0.4%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2409
100.0%

Most frequent character per script

ValueCountFrequency (%)
0842
35.0%
.801
33.3%
1664
27.6%
596
 
4.0%
26
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2409
100.0%

Most frequent character per block

ValueCountFrequency (%)
0842
35.0%
.801
33.3%
1664
27.6%
596
 
4.0%
26
 
0.2%

against_poison
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9753433208
Minimum0
Maximum4
Zeros46
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:31.494205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5493752481
Coefficient of variation (CV)0.5632634544
Kurtosis5.085348539
Mean0.9753433208
Median Absolute Deviation (MAD)0
Skewness1.373468375
Sum781.25
Variance0.3018131632
MonotonicityNot monotonic
2022-01-06T15:32:31.593426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1476
59.4%
0.5153
 
19.1%
2102
 
12.7%
046
 
5.7%
0.2519
 
2.4%
45
 
0.6%
ValueCountFrequency (%)
046
 
5.7%
0.2519
 
2.4%
0.5153
 
19.1%
1476
59.4%
2102
 
12.7%
ValueCountFrequency (%)
45
 
0.6%
2102
 
12.7%
1476
59.4%
0.5153
 
19.1%
0.2519
 
2.4%

against_psychic
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.005305868
Minimum0
Maximum4
Zeros46
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:31.699688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4951829839
Coefficient of variation (CV)0.4925694755
Kurtosis3.624818834
Mean1.005305868
Median Absolute Deviation (MAD)0
Skewness0.9370893774
Sum805.25
Variance0.2452061876
MonotonicityNot monotonic
2022-01-06T15:32:31.800185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1539
67.3%
0.5105
 
13.1%
2102
 
12.7%
046
 
5.7%
0.257
 
0.9%
42
 
0.2%
ValueCountFrequency (%)
046
 
5.7%
0.257
 
0.9%
0.5105
 
13.1%
1539
67.3%
2102
 
12.7%
ValueCountFrequency (%)
42
 
0.2%
2102
 
12.7%
1539
67.3%
0.5105
 
13.1%
0.257
 
0.9%

against_rock
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
449 
2.0
198 
0.5
127 
4.0
 
23
0.25
 
4

Length

Max length4
Median length3
Mean length3.004993758
Min length3

Characters and Unicode

Total characters2407
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
1.0449
56.1%
2.0198
24.7%
0.5127
 
15.9%
4.023
 
2.9%
0.254
 
0.5%
2022-01-06T15:32:32.029605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:32.111484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0449
56.1%
2.0198
24.7%
0.5127
 
15.9%
4.023
 
2.9%
0.254
 
0.5%

Most occurring characters

ValueCountFrequency (%)
.801
33.3%
0801
33.3%
1449
18.7%
2202
 
8.4%
5131
 
5.4%
423
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1606
66.7%
Other Punctuation801
33.3%

Most frequent character per category

ValueCountFrequency (%)
0801
49.9%
1449
28.0%
2202
 
12.6%
5131
 
8.2%
423
 
1.4%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2407
100.0%

Most frequent character per script

ValueCountFrequency (%)
.801
33.3%
0801
33.3%
1449
18.7%
2202
 
8.4%
5131
 
5.4%
423
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2407
100.0%

Most frequent character per block

ValueCountFrequency (%)
.801
33.3%
0801
33.3%
1449
18.7%
2202
 
8.4%
5131
 
5.4%
423
 
1.0%

against_steel
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
451 
0.5
237 
2.0
100 
0.25
 
9
4.0
 
4

Length

Max length4
Median length3
Mean length3.011235955
Min length3

Characters and Unicode

Total characters2412
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.5
5th row0.5
ValueCountFrequency (%)
1.0451
56.3%
0.5237
29.6%
2.0100
 
12.5%
0.259
 
1.1%
4.04
 
0.5%
2022-01-06T15:32:32.367324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:32.460468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0451
56.3%
0.5237
29.6%
2.0100
 
12.5%
0.259
 
1.1%
4.04
 
0.5%

Most occurring characters

ValueCountFrequency (%)
.801
33.2%
0801
33.2%
1451
18.7%
5246
 
10.2%
2109
 
4.5%
44
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1611
66.8%
Other Punctuation801
33.2%

Most frequent character per category

ValueCountFrequency (%)
0801
49.7%
1451
28.0%
5246
 
15.3%
2109
 
6.8%
44
 
0.2%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2412
100.0%

Most frequent character per script

ValueCountFrequency (%)
.801
33.2%
0801
33.2%
1451
18.7%
5246
 
10.2%
2109
 
4.5%
44
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2412
100.0%

Most frequent character per block

ValueCountFrequency (%)
.801
33.2%
0801
33.2%
1451
18.7%
5246
 
10.2%
2109
 
4.5%
44
 
0.2%

against_water
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1.0
426 
0.5
229 
2.0
129 
4.0
 
12
0.25
 
5

Length

Max length4
Median length3
Mean length3.006242197
Min length3

Characters and Unicode

Total characters2408
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row2.0
5th row2.0
ValueCountFrequency (%)
1.0426
53.2%
0.5229
28.6%
2.0129
 
16.1%
4.012
 
1.5%
0.255
 
0.6%
2022-01-06T15:32:32.658420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:32.732465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0426
53.2%
0.5229
28.6%
2.0129
 
16.1%
4.012
 
1.5%
0.255
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0801
33.3%
.801
33.3%
1426
17.7%
5234
 
9.7%
2134
 
5.6%
412
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1607
66.7%
Other Punctuation801
33.3%

Most frequent character per category

ValueCountFrequency (%)
0801
49.8%
1426
26.5%
5234
 
14.6%
2134
 
8.3%
412
 
0.7%
ValueCountFrequency (%)
.801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2408
100.0%

Most frequent character per script

ValueCountFrequency (%)
0801
33.3%
.801
33.3%
1426
17.7%
5234
 
9.7%
2134
 
5.6%
412
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2408
100.0%

Most frequent character per block

ValueCountFrequency (%)
0801
33.3%
.801
33.3%
1426
17.7%
5234
 
9.7%
2134
 
5.6%
412
 
0.5%

attack
Real number (ℝ≥0)

Distinct114
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.8576779
Minimum5
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:32.835159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q155
median75
Q3100
95-th percentile135
Maximum185
Range180
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.15882024
Coefficient of variation (CV)0.4130462288
Kurtosis0.0713368317
Mean77.8576779
Median Absolute Deviation (MAD)22
Skewness0.5308107399
Sum62364
Variance1034.189719
MonotonicityNot monotonic
2022-01-06T15:32:32.979381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10038
 
4.7%
6035
 
4.4%
6535
 
4.4%
5535
 
4.4%
7534
 
4.2%
5034
 
4.2%
8033
 
4.1%
7032
 
4.0%
8532
 
4.0%
9027
 
3.4%
Other values (104)466
58.2%
ValueCountFrequency (%)
52
 
0.2%
103
 
0.4%
151
 
0.1%
208
1.0%
221
 
0.1%
ValueCountFrequency (%)
1851
 
0.1%
1811
 
0.1%
1802
0.2%
1701
 
0.1%
1653
0.4%

base_egg_steps
Real number (ℝ≥0)

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7191.011236
Minimum1280
Maximum30720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:33.263673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1280
5-th percentile3840
Q15120
median5120
Q36400
95-th percentile30720
Maximum30720
Range29440
Interquartile range (IQR)1280

Descriptive statistics

Standard deviation6558.220422
Coefficient of variation (CV)0.9120025274
Kurtosis7.58130318
Mean7191.011236
Median Absolute Deviation (MAD)0
Skewness2.955754452
Sum5760000
Variance43010255.1
MonotonicityNot monotonic
2022-01-06T15:32:33.356364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5120436
54.4%
3840140
 
17.5%
640055
 
6.9%
3072049
 
6.1%
1024042
 
5.2%
768026
 
3.2%
256022
 
2.7%
2048016
 
2.0%
896013
 
1.6%
12802
 
0.2%
ValueCountFrequency (%)
12802
 
0.2%
256022
 
2.7%
3840140
 
17.5%
5120436
54.4%
640055
 
6.9%
ValueCountFrequency (%)
3072049
6.1%
2048016
 
2.0%
1024042
5.2%
896013
 
1.6%
768026
3.2%

base_happiness
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.36204744
Minimum0
Maximum140
Zeros36
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:33.457120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q170
median70
Q370
95-th percentile70
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.59894787
Coefficient of variation (CV)0.2998521105
Kurtosis5.936989315
Mean65.36204744
Median Absolute Deviation (MAD)0
Skewness-1.182298979
Sum52355
Variance384.1187578
MonotonicityNot monotonic
2022-01-06T15:32:33.555417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
70667
83.3%
3569
 
8.6%
036
 
4.5%
10014
 
1.7%
14010
 
1.2%
905
 
0.6%
ValueCountFrequency (%)
036
 
4.5%
3569
 
8.6%
70667
83.3%
905
 
0.6%
10014
 
1.7%
ValueCountFrequency (%)
14010
 
1.2%
10014
 
1.7%
905
 
0.6%
70667
83.3%
3569
 
8.6%

base_total
Real number (ℝ≥0)

Distinct203
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean428.3770287
Minimum180
Maximum780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:33.692792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile250
Q1320
median435
Q3505
95-th percentile618
Maximum780
Range600
Interquartile range (IQR)185

Descriptive statistics

Standard deviation119.2035766
Coefficient of variation (CV)0.2782679009
Kurtosis-0.5279579753
Mean428.3770287
Median Absolute Deviation (MAD)94
Skewness0.1745927583
Sum343130
Variance14209.49267
MonotonicityNot monotonic
2022-01-06T15:32:33.849189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40526
 
3.2%
60026
 
3.2%
50021
 
2.6%
30021
 
2.6%
58020
 
2.5%
49018
 
2.2%
48516
 
2.0%
49516
 
2.0%
48015
 
1.9%
33015
 
1.9%
Other values (193)607
75.8%
ValueCountFrequency (%)
1801
 
0.1%
1901
 
0.1%
1941
 
0.1%
1953
0.4%
1981
 
0.1%
ValueCountFrequency (%)
7802
 
0.2%
7702
 
0.2%
7201
 
0.1%
7081
 
0.1%
7008
1.0%

capture_rate
Categorical

Distinct34
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
45
250 
190
75 
255
69 
75
61 
3
58 
Other values (29)
288 

Length

Max length24
Median length2
Mean length2.314606742
Min length1

Characters and Unicode

Total characters1854
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.7%

Sample

1st row45
2nd row45
3rd row45
4th row45
5th row45
ValueCountFrequency (%)
45250
31.2%
19075
 
9.4%
25569
 
8.6%
7561
 
7.6%
358
 
7.2%
12055
 
6.9%
6050
 
6.2%
9038
 
4.7%
3020
 
2.5%
20019
 
2.4%
Other values (24)106
13.2%
2022-01-06T15:32:34.144968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45250
31.1%
19075
 
9.3%
25569
 
8.6%
7561
 
7.6%
358
 
7.2%
12055
 
6.8%
6050
 
6.2%
9038
 
4.7%
3021
 
2.6%
20019
 
2.4%
Other values (25)107
13.3%

Most occurring characters

ValueCountFrequency (%)
5519
28.0%
0334
18.0%
4257
13.9%
2207
 
11.2%
1177
 
9.5%
9113
 
6.1%
389
 
4.8%
772
 
3.9%
654
 
2.9%
813
 
0.7%
Other values (10)19
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1835
99.0%
Lowercase Letter11
 
0.6%
Space Separator2
 
0.1%
Open Punctuation2
 
0.1%
Uppercase Letter2
 
0.1%
Close Punctuation2
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
5519
28.3%
0334
18.2%
4257
14.0%
2207
 
11.3%
1177
 
9.6%
9113
 
6.2%
389
 
4.9%
772
 
3.9%
654
 
2.9%
813
 
0.7%
ValueCountFrequency (%)
e4
36.4%
t2
18.2%
o2
18.2%
r2
18.2%
i1
 
9.1%
ValueCountFrequency (%)
M1
50.0%
C1
50.0%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
(2
100.0%
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1841
99.3%
Latin13
 
0.7%

Most frequent character per script

ValueCountFrequency (%)
5519
28.2%
0334
18.1%
4257
14.0%
2207
 
11.2%
1177
 
9.6%
9113
 
6.1%
389
 
4.8%
772
 
3.9%
654
 
2.9%
813
 
0.7%
Other values (3)6
 
0.3%
ValueCountFrequency (%)
e4
30.8%
t2
15.4%
o2
15.4%
r2
15.4%
M1
 
7.7%
i1
 
7.7%
C1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1854
100.0%

Most frequent character per block

ValueCountFrequency (%)
5519
28.0%
0334
18.0%
4257
13.9%
2207
 
11.2%
1177
 
9.5%
9113
 
6.1%
389
 
4.8%
772
 
3.9%
654
 
2.9%
813
 
0.7%
Other values (10)19
 
1.0%

classfication
Categorical

HIGH CARDINALITY
UNIFORM

Distinct588
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Dragon Pokémon
 
8
Mouse Pokémon
 
6
Mushroom Pokémon
 
6
Flame Pokémon
 
5
Balloon Pokémon
 
5
Other values (583)
771 

Length

Max length51
Median length16
Mean length15.6267166
Min length11

Characters and Unicode

Total characters12517
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique440 ?
Unique (%)54.9%

Sample

1st rowSeed Pokémon
2nd rowSeed Pokémon
3rd rowSeed Pokémon
4th rowLizard Pokémon
5th rowFlame Pokémon
ValueCountFrequency (%)
Dragon Pokémon8
 
1.0%
Mouse Pokémon6
 
0.7%
Mushroom Pokémon6
 
0.7%
Flame Pokémon5
 
0.6%
Balloon Pokémon5
 
0.6%
Fox Pokémon5
 
0.6%
Fairy Pokémon5
 
0.6%
Seed Pokémon4
 
0.5%
Bat Pokémon4
 
0.5%
Drill Pokémon4
 
0.5%
Other values (578)749
93.5%
2022-01-06T15:32:34.462539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pokémon801
43.3%
sea15
 
0.8%
bird14
 
0.8%
poison10
 
0.5%
iron10
 
0.5%
mouse9
 
0.5%
tiny9
 
0.5%
water8
 
0.4%
fish8
 
0.4%
dragon8
 
0.4%
Other values (595)956
51.7%

Most occurring characters

ValueCountFrequency (%)
o2032
16.2%
n1158
 
9.3%
1047
 
8.4%
m889
 
7.1%
k881
 
7.0%
P871
 
7.0%
é802
 
6.4%
e558
 
4.5%
a429
 
3.4%
r423
 
3.4%
Other values (46)3427
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9599
76.7%
Uppercase Letter1862
 
14.9%
Space Separator1047
 
8.4%
Dash Punctuation5
 
< 0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o2032
21.2%
n1158
12.1%
m889
9.3%
k881
9.2%
é802
 
8.4%
e558
 
5.8%
a429
 
4.5%
r423
 
4.4%
i420
 
4.4%
l330
 
3.4%
Other values (17)1677
17.5%
ValueCountFrequency (%)
P871
46.8%
S162
 
8.7%
B114
 
6.1%
C86
 
4.6%
F82
 
4.4%
M72
 
3.9%
T62
 
3.3%
L54
 
2.9%
D54
 
2.9%
W47
 
2.5%
Other values (15)258
 
13.9%
ValueCountFrequency (%)
1047
100.0%
ValueCountFrequency (%)
-5
100.0%
ValueCountFrequency (%)
(2
100.0%
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11461
91.6%
Common1056
 
8.4%

Most frequent character per script

ValueCountFrequency (%)
o2032
17.7%
n1158
 
10.1%
m889
 
7.8%
k881
 
7.7%
P871
 
7.6%
é802
 
7.0%
e558
 
4.9%
a429
 
3.7%
r423
 
3.7%
i420
 
3.7%
Other values (42)2998
26.2%
ValueCountFrequency (%)
1047
99.1%
-5
 
0.5%
(2
 
0.2%
)2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11715
93.6%
Latin 1 Sup802
 
6.4%

Most frequent character per block

ValueCountFrequency (%)
o2032
17.3%
n1158
 
9.9%
1047
 
8.9%
m889
 
7.6%
k881
 
7.5%
P871
 
7.4%
e558
 
4.8%
a429
 
3.7%
r423
 
3.6%
i420
 
3.6%
Other values (45)3007
25.7%
ValueCountFrequency (%)
é802
100.0%

defense
Real number (ℝ≥0)

Distinct109
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.00873908
Minimum5
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:34.635529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q150
median70
Q390
95-th percentile130
Maximum230
Range225
Interquartile range (IQR)40

Descriptive statistics

Standard deviation30.76915945
Coefficient of variation (CV)0.4214448824
Kurtosis2.583358891
Mean73.00873908
Median Absolute Deviation (MAD)20
Skewness1.121058307
Sum58480
Variance946.7411735
MonotonicityNot monotonic
2022-01-06T15:32:34.782952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5051
 
6.4%
7049
 
6.1%
6046
 
5.7%
8041
 
5.1%
4039
 
4.9%
9033
 
4.1%
6533
 
4.1%
4532
 
4.0%
5530
 
3.7%
10029
 
3.6%
Other values (99)418
52.2%
ValueCountFrequency (%)
52
0.2%
101
 
0.1%
154
0.5%
203
0.4%
231
 
0.1%
ValueCountFrequency (%)
2303
0.4%
2001
 
0.1%
1841
 
0.1%
1802
0.2%
1681
 
0.1%

experience_growth
Real number (ℝ≥0)

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1054995.905
Minimum600000
Maximum1640000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:34.906713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum600000
5-th percentile800000
Q11000000
median1000000
Q31059860
95-th percentile1250000
Maximum1640000
Range1040000
Interquartile range (IQR)59860

Descriptive statistics

Standard deviation160255.8351
Coefficient of variation (CV)0.1519018551
Kurtosis2.852907724
Mean1054995.905
Median Absolute Deviation (MAD)59860
Skewness0.3111277699
Sum845051720
Variance2.568193268 × 1010
MonotonicityNot monotonic
2022-01-06T15:32:35.015324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1000000335
41.8%
1059860202
25.2%
1250000172
21.5%
80000056
 
7.0%
60000022
 
2.7%
164000014
 
1.7%
ValueCountFrequency (%)
60000022
 
2.7%
80000056
 
7.0%
1000000335
41.8%
1059860202
25.2%
1250000172
21.5%
ValueCountFrequency (%)
164000014
 
1.7%
1250000172
21.5%
1059860202
25.2%
1000000335
41.8%
80000056
 
7.0%

height_m
Real number (ℝ≥0)

MISSING

Distinct51
Distinct (%)6.5%
Missing20
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1.163892446
Minimum0.1
Maximum14.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:35.141498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.6
median1
Q31.5
95-th percentile2.5
Maximum14.5
Range14.4
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.080326261
Coefficient of variation (CV)0.9282011106
Kurtosis43.10465652
Mean1.163892446
Median Absolute Deviation (MAD)0.5
Skewness5.08001571
Sum909
Variance1.167104829
MonotonicityNot monotonic
2022-01-06T15:32:35.288923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.670
 
8.7%
0.560
 
7.5%
0.459
 
7.4%
157
 
7.1%
0.353
 
6.6%
1.249
 
6.1%
0.843
 
5.4%
1.543
 
5.4%
0.737
 
4.6%
1.136
 
4.5%
Other values (41)274
34.2%
ValueCountFrequency (%)
0.15
 
0.6%
0.217
 
2.1%
0.353
6.6%
0.459
7.4%
0.560
7.5%
ValueCountFrequency (%)
14.51
0.1%
9.22
0.2%
8.81
0.1%
71
0.1%
6.51
0.1%

hp
Real number (ℝ≥0)

Distinct99
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.9588015
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:35.439214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q150
median65
Q380
95-th percentile110
Maximum255
Range254
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.57601457
Coefficient of variation (CV)0.3853897399
Kurtosis8.334972814
Mean68.9588015
Median Absolute Deviation (MAD)15
Skewness1.826590534
Sum55236
Variance706.2845506
MonotonicityNot monotonic
2022-01-06T15:32:35.595522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6068
 
8.5%
7055
 
6.9%
5054
 
6.7%
7544
 
5.5%
6544
 
5.5%
4542
 
5.2%
4041
 
5.1%
8038
 
4.7%
5537
 
4.6%
10028
 
3.5%
Other values (89)350
43.7%
ValueCountFrequency (%)
11
 
0.1%
101
 
0.1%
206
0.7%
253
0.4%
281
 
0.1%
ValueCountFrequency (%)
2551
0.1%
2501
0.1%
2231
0.1%
2161
0.1%
1901
0.1%

japanese_name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Fushigidaneフシギダネ
 
1
Dagekiダゲキ
 
1
Mogurewモグリュー
 
1
Doryuzuドリュウズ
 
1
Tabunneタブンネ
 
1
Other values (796)
796 

Length

Max length33
Median length12
Mean length12.27091136
Min length4

Characters and Unicode

Total characters9829
Distinct characters146
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique801 ?
Unique (%)100.0%

Sample

1st rowFushigidaneフシギダネ
2nd rowFushigisouフシギソウ
3rd rowFushigibanaフシギバナ
4th rowHitokageヒトカゲ
5th rowLizardoリザード
ValueCountFrequency (%)
Fushigidaneフシギダネ1
 
0.1%
Dagekiダゲキ1
 
0.1%
Mogurewモグリュー1
 
0.1%
Doryuzuドリュウズ1
 
0.1%
Tabunneタブンネ1
 
0.1%
Dokkorerドッコラー1
 
0.1%
Dotekkotsuドテッコツ1
 
0.1%
Roubushinローブシン1
 
0.1%
Otamaroオタマロ1
 
0.1%
Gamagaruガマガル1
 
0.1%
Other values (791)791
98.8%
2022-01-06T15:32:35.895493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no4
 
0.5%
keshin3
 
0.4%
desukarnデスカーン1
 
0.1%
kurumiruクルミル1
 
0.1%
kurumayuクルマユ1
 
0.1%
hahakomoriハハコモリ1
 
0.1%
fushideフシデ1
 
0.1%
wheegaホイーガ1
 
0.1%
pendrorペンドラー1
 
0.1%
monmenモンメン1
 
0.1%
Other values (817)817
98.2%

Most occurring characters

ValueCountFrequency (%)
a721
 
7.3%
o570
 
5.8%
i473
 
4.8%
r427
 
4.3%
e425
 
4.3%
u383
 
3.9%
n338
 
3.4%
263
 
2.7%
215
 
2.2%
s211
 
2.1%
Other values (136)5803
59.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5268
53.6%
Other Letter3401
34.6%
Uppercase Letter820
 
8.3%
Modifier Letter263
 
2.7%
Space Separator31
 
0.3%
Open Punctuation13
 
0.1%
Close Punctuation13
 
0.1%
Other Punctuation9
 
0.1%
Dash Punctuation5
 
0.1%
Decimal Number4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
215
 
6.3%
177
 
5.2%
133
 
3.9%
113
 
3.3%
105
 
3.1%
102
 
3.0%
95
 
2.8%
94
 
2.8%
90
 
2.6%
89
 
2.6%
Other values (68)2188
64.3%
ValueCountFrequency (%)
M80
 
9.8%
K79
 
9.6%
G52
 
6.3%
D52
 
6.3%
N51
 
6.2%
S50
 
6.1%
H44
 
5.4%
B44
 
5.4%
P43
 
5.2%
T38
 
4.6%
Other values (16)287
35.0%
ValueCountFrequency (%)
a721
13.7%
o570
10.8%
i473
 
9.0%
r427
 
8.1%
e425
 
8.1%
u383
 
7.3%
n338
 
6.4%
s211
 
4.0%
k194
 
3.7%
m194
 
3.7%
Other values (16)1332
25.3%
ValueCountFrequency (%)
4
44.4%
?2
22.2%
%1
 
11.1%
:1
 
11.1%
1
 
11.1%
ValueCountFrequency (%)
21
25.0%
1
25.0%
11
25.0%
01
25.0%
ValueCountFrequency (%)
1
50.0%
1
50.0%
ValueCountFrequency (%)
263
100.0%
ValueCountFrequency (%)
31
100.0%
ValueCountFrequency (%)
(13
100.0%
ValueCountFrequency (%)
)13
100.0%
ValueCountFrequency (%)
-5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6088
61.9%
Katakana3401
34.6%
Common340
 
3.5%

Most frequent character per script

ValueCountFrequency (%)
215
 
6.3%
177
 
5.2%
133
 
3.9%
113
 
3.3%
105
 
3.1%
102
 
3.0%
95
 
2.8%
94
 
2.8%
90
 
2.6%
89
 
2.6%
Other values (68)2188
64.3%
ValueCountFrequency (%)
a721
 
11.8%
o570
 
9.4%
i473
 
7.8%
r427
 
7.0%
e425
 
7.0%
u383
 
6.3%
n338
 
5.6%
s211
 
3.5%
k194
 
3.2%
m194
 
3.2%
Other values (42)2152
35.3%
ValueCountFrequency (%)
263
77.4%
31
 
9.1%
(13
 
3.8%
)13
 
3.8%
-5
 
1.5%
4
 
1.2%
?2
 
0.6%
1
 
0.3%
1
 
0.3%
21
 
0.3%
Other values (6)6
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII6156
62.6%
Katakana3668
37.3%
None3
 
< 0.1%
Misc Symbols2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
a721
 
11.7%
o570
 
9.3%
i473
 
7.7%
r427
 
6.9%
e425
 
6.9%
u383
 
6.2%
n338
 
5.5%
s211
 
3.4%
k194
 
3.2%
m194
 
3.2%
Other values (51)2220
36.1%
ValueCountFrequency (%)
263
 
7.2%
215
 
5.9%
177
 
4.8%
133
 
3.6%
113
 
3.1%
105
 
2.9%
102
 
2.8%
95
 
2.6%
94
 
2.6%
90
 
2.5%
Other values (70)2281
62.2%
ValueCountFrequency (%)
1
50.0%
1
50.0%
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Bulbasaur
 
1
Sawk
 
1
Drilbur
 
1
Excadrill
 
1
Audino
 
1
Other values (796)
796 

Length

Max length12
Median length7
Mean length7.465667915
Min length3

Characters and Unicode

Total characters5980
Distinct characters61
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique801 ?
Unique (%)100.0%

Sample

1st rowBulbasaur
2nd rowIvysaur
3rd rowVenusaur
4th rowCharmander
5th rowCharmeleon
ValueCountFrequency (%)
Bulbasaur1
 
0.1%
Sawk1
 
0.1%
Drilbur1
 
0.1%
Excadrill1
 
0.1%
Audino1
 
0.1%
Timburr1
 
0.1%
Gurdurr1
 
0.1%
Conkeldurr1
 
0.1%
Tympole1
 
0.1%
Palpitoad1
 
0.1%
Other values (791)791
98.8%
2022-01-06T15:32:36.339134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tapu4
 
0.5%
mime2
 
0.2%
bulbasaur1
 
0.1%
drilbur1
 
0.1%
audino1
 
0.1%
timburr1
 
0.1%
gurdurr1
 
0.1%
conkeldurr1
 
0.1%
tympole1
 
0.1%
palpitoad1
 
0.1%
Other values (794)794
98.3%

Most occurring characters

ValueCountFrequency (%)
a547
 
9.1%
e502
 
8.4%
o490
 
8.2%
i436
 
7.3%
r433
 
7.2%
l347
 
5.8%
n346
 
5.8%
t273
 
4.6%
u248
 
4.1%
s195
 
3.3%
Other values (51)2163
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5151
86.1%
Uppercase Letter810
 
13.5%
Space Separator7
 
0.1%
Dash Punctuation5
 
0.1%
Other Punctuation4
 
0.1%
Other Symbol2
 
< 0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a547
10.6%
e502
 
9.7%
o490
 
9.5%
i436
 
8.5%
r433
 
8.4%
l347
 
6.7%
n346
 
6.7%
t273
 
5.3%
u248
 
4.8%
s195
 
3.8%
Other values (17)1334
25.9%
ValueCountFrequency (%)
S111
13.7%
M67
 
8.3%
C63
 
7.8%
P55
 
6.8%
G51
 
6.3%
T49
 
6.0%
D46
 
5.7%
B45
 
5.6%
L39
 
4.8%
A34
 
4.2%
Other values (16)250
30.9%
ValueCountFrequency (%)
.2
50.0%
'1
25.0%
:1
25.0%
ValueCountFrequency (%)
1
50.0%
1
50.0%
ValueCountFrequency (%)
7
100.0%
ValueCountFrequency (%)
21
100.0%
ValueCountFrequency (%)
-5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5961
99.7%
Common19
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
a547
 
9.2%
e502
 
8.4%
o490
 
8.2%
i436
 
7.3%
r433
 
7.3%
l347
 
5.8%
n346
 
5.8%
t273
 
4.6%
u248
 
4.2%
s195
 
3.3%
Other values (43)2144
36.0%
ValueCountFrequency (%)
7
36.8%
-5
26.3%
.2
 
10.5%
1
 
5.3%
1
 
5.3%
'1
 
5.3%
21
 
5.3%
:1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5976
99.9%
Misc Symbols2
 
< 0.1%
Latin 1 Sup2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
a547
 
9.2%
e502
 
8.4%
o490
 
8.2%
i436
 
7.3%
r433
 
7.2%
l347
 
5.8%
n346
 
5.8%
t273
 
4.6%
u248
 
4.1%
s195
 
3.3%
Other values (48)2159
36.1%
ValueCountFrequency (%)
1
50.0%
1
50.0%
ValueCountFrequency (%)
é2
100.0%

percentage_male
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)1.0%
Missing98
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean55.15576102
Minimum0
Maximum100
Zeros27
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:36.442591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.6
Q150
median50
Q350
95-th percentile88.1
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.26162268
Coefficient of variation (CV)0.3673527896
Kurtosis1.159100512
Mean55.15576102
Median Absolute Deviation (MAD)0
Skewness0.06634680374
Sum38774.5
Variance410.5333535
MonotonicityNot monotonic
2022-01-06T15:32:36.541271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
50501
62.5%
88.1111
 
13.9%
027
 
3.4%
24.624
 
3.0%
10019
 
2.4%
75.419
 
2.4%
11.22
 
0.2%
(Missing)98
 
12.2%
ValueCountFrequency (%)
027
 
3.4%
11.22
 
0.2%
24.624
 
3.0%
50501
62.5%
75.419
 
2.4%
ValueCountFrequency (%)
10019
 
2.4%
88.1111
 
13.9%
75.419
 
2.4%
50501
62.5%
24.624
 
3.0%

pokedex_number
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean401
Minimum1
Maximum801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:36.669131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1201
median401
Q3601
95-th percentile761
Maximum801
Range800
Interquartile range (IQR)400

Descriptive statistics

Standard deviation231.3730754
Coefficient of variation (CV)0.5769902129
Kurtosis-1.2
Mean401
Median Absolute Deviation (MAD)200
Skewness0
Sum321201
Variance53533.5
MonotonicityStrictly increasing
2022-01-06T15:32:36.825326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
5391
 
0.1%
5291
 
0.1%
5301
 
0.1%
5311
 
0.1%
5321
 
0.1%
5331
 
0.1%
5341
 
0.1%
5351
 
0.1%
5361
 
0.1%
Other values (791)791
98.8%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
ValueCountFrequency (%)
8011
0.1%
8001
0.1%
7991
0.1%
7981
0.1%
7971
0.1%

sp_attack
Real number (ℝ≥0)

Distinct111
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.30586767
Minimum10
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:36.986561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q145
median65
Q391
95-th percentile131
Maximum194
Range184
Interquartile range (IQR)46

Descriptive statistics

Standard deviation32.35382633
Coefficient of variation (CV)0.4537330151
Kurtosis0.4124864058
Mean71.30586767
Median Absolute Deviation (MAD)21
Skewness0.778370766
Sum57116
Variance1046.770078
MonotonicityNot monotonic
2022-01-06T15:32:37.143341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4052
 
6.5%
6048
 
6.0%
6543
 
5.4%
5042
 
5.2%
5538
 
4.7%
4534
 
4.2%
7030
 
3.7%
9530
 
3.7%
3529
 
3.6%
3028
 
3.5%
Other values (101)427
53.3%
ValueCountFrequency (%)
104
0.5%
154
0.5%
208
1.0%
231
 
0.1%
242
 
0.2%
ValueCountFrequency (%)
1941
 
0.1%
1802
0.2%
1751
 
0.1%
1731
 
0.1%
1703
0.4%

sp_defense
Real number (ℝ≥0)

Distinct97
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.9113608
Minimum20
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:37.298860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile31
Q150
median66
Q390
95-th percentile120
Maximum230
Range210
Interquartile range (IQR)40

Descriptive statistics

Standard deviation27.94250138
Coefficient of variation (CV)0.3940483029
Kurtosis1.525919002
Mean70.9113608
Median Absolute Deviation (MAD)19
Skewness0.8676202646
Sum56800
Variance780.7833833
MonotonicityNot monotonic
2022-01-06T15:32:37.444657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5053
 
6.6%
6044
 
5.5%
6542
 
5.2%
5542
 
5.2%
7041
 
5.1%
8041
 
5.1%
7539
 
4.9%
4538
 
4.7%
4033
 
4.1%
9032
 
4.0%
Other values (87)396
49.4%
ValueCountFrequency (%)
205
 
0.6%
231
 
0.1%
2511
1.4%
3022
2.7%
313
 
0.4%
ValueCountFrequency (%)
2301
 
0.1%
2001
 
0.1%
1601
 
0.1%
1543
0.4%
1505
0.6%

speed
Real number (ℝ≥0)

Distinct113
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.33458177
Minimum5
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:37.592651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q145
median65
Q385
95-th percentile115
Maximum180
Range175
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.90766188
Coefficient of variation (CV)0.4357856958
Kurtosis-0.1186680138
Mean66.33458177
Median Absolute Deviation (MAD)20
Skewness0.4389181782
Sum53134
Variance835.6529151
MonotonicityNot monotonic
2022-01-06T15:32:37.729326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6046
 
5.7%
5043
 
5.4%
6538
 
4.7%
4536
 
4.5%
7036
 
4.5%
3035
 
4.4%
4034
 
4.2%
8029
 
3.6%
5528
 
3.5%
8528
 
3.5%
Other values (103)448
55.9%
ValueCountFrequency (%)
53
 
0.4%
103
 
0.4%
1511
1.4%
2015
1.9%
221
 
0.1%
ValueCountFrequency (%)
1801
 
0.1%
1601
 
0.1%
1511
 
0.1%
1503
0.4%
1453
0.4%

type1
Categorical

Distinct18
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
water
114 
normal
105 
grass
78 
bug
72 
psychic
53 
Other values (13)
379 

Length

Max length8
Median length5
Mean length5.237203496
Min length3

Characters and Unicode

Total characters4195
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrass
2nd rowgrass
3rd rowgrass
4th rowfire
5th rowfire
ValueCountFrequency (%)
water114
14.2%
normal105
13.1%
grass78
9.7%
bug72
9.0%
psychic53
 
6.6%
fire52
 
6.5%
rock45
 
5.6%
electric39
 
4.9%
poison32
 
4.0%
ground32
 
4.0%
Other values (8)179
22.3%
2022-01-06T15:32:38.041915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
water114
14.2%
normal105
13.1%
grass78
9.7%
bug72
9.0%
psychic53
 
6.6%
fire52
 
6.5%
rock45
 
5.6%
electric39
 
4.9%
poison32
 
4.0%
ground32
 
4.0%
Other values (8)179
22.3%

Most occurring characters

ValueCountFrequency (%)
r539
12.8%
a371
 
8.8%
e315
 
7.5%
o300
 
7.2%
g295
 
7.0%
s292
 
7.0%
i276
 
6.6%
c252
 
6.0%
t232
 
5.5%
n227
 
5.4%
Other values (11)1096
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4195
100.0%

Most frequent character per category

ValueCountFrequency (%)
r539
12.8%
a371
 
8.8%
e315
 
7.5%
o300
 
7.2%
g295
 
7.0%
s292
 
7.0%
i276
 
6.6%
c252
 
6.0%
t232
 
5.5%
n227
 
5.4%
Other values (11)1096
26.1%

Most occurring scripts

ValueCountFrequency (%)
Latin4195
100.0%

Most frequent character per script

ValueCountFrequency (%)
r539
12.8%
a371
 
8.8%
e315
 
7.5%
o300
 
7.2%
g295
 
7.0%
s292
 
7.0%
i276
 
6.6%
c252
 
6.0%
t232
 
5.5%
n227
 
5.4%
Other values (11)1096
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4195
100.0%

Most frequent character per block

ValueCountFrequency (%)
r539
12.8%
a371
 
8.8%
e315
 
7.5%
o300
 
7.2%
g295
 
7.0%
s292
 
7.0%
i276
 
6.6%
c252
 
6.0%
t232
 
5.5%
n227
 
5.4%
Other values (11)1096
26.1%

type2
Categorical

MISSING

Distinct18
Distinct (%)4.3%
Missing384
Missing (%)47.9%
Memory size6.4 KiB
flying
95 
poison
34 
ground
34 
psychic
29 
fairy
29 
Other values (13)
196 

Length

Max length8
Median length6
Mean length5.613908873
Min length3

Characters and Unicode

Total characters2341
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpoison
2nd rowpoison
3rd rowpoison
4th rowflying
5th rowflying
ValueCountFrequency (%)
flying95
 
11.9%
poison34
 
4.2%
ground34
 
4.2%
psychic29
 
3.6%
fairy29
 
3.6%
fighting25
 
3.1%
steel22
 
2.7%
dark21
 
2.6%
grass20
 
2.5%
water17
 
2.1%
Other values (8)91
 
11.4%
(Missing)384
47.9%
2022-01-06T15:32:38.314225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flying95
22.8%
poison34
 
8.2%
ground34
 
8.2%
fairy29
 
7.0%
psychic29
 
7.0%
fighting25
 
6.0%
steel22
 
5.3%
dark21
 
5.0%
grass20
 
4.8%
water17
 
4.1%
Other values (8)91
21.8%

Most occurring characters

ValueCountFrequency (%)
i274
11.7%
g235
 
10.0%
n209
 
8.9%
r178
 
7.6%
f162
 
6.9%
y153
 
6.5%
o151
 
6.5%
s139
 
5.9%
l130
 
5.6%
a108
 
4.6%
Other values (11)602
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2341
100.0%

Most frequent character per category

ValueCountFrequency (%)
i274
11.7%
g235
 
10.0%
n209
 
8.9%
r178
 
7.6%
f162
 
6.9%
y153
 
6.5%
o151
 
6.5%
s139
 
5.9%
l130
 
5.6%
a108
 
4.6%
Other values (11)602
25.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2341
100.0%

Most frequent character per script

ValueCountFrequency (%)
i274
11.7%
g235
 
10.0%
n209
 
8.9%
r178
 
7.6%
f162
 
6.9%
y153
 
6.5%
o151
 
6.5%
s139
 
5.9%
l130
 
5.6%
a108
 
4.6%
Other values (11)602
25.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2341
100.0%

Most frequent character per block

ValueCountFrequency (%)
i274
11.7%
g235
 
10.0%
n209
 
8.9%
r178
 
7.6%
f162
 
6.9%
y153
 
6.5%
o151
 
6.5%
s139
 
5.9%
l130
 
5.6%
a108
 
4.6%
Other values (11)602
25.7%

weight_kg
Real number (ℝ≥0)

MISSING

Distinct421
Distinct (%)53.9%
Missing20
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean61.37810499
Minimum0.1
Maximum999.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:38.448376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.5
Q19
median27.3
Q364.8
95-th percentile230
Maximum999.9
Range999.8
Interquartile range (IQR)55.8

Descriptive statistics

Standard deviation109.3547659
Coefficient of variation (CV)1.781657577
Kurtosis31.73581997
Mean61.37810499
Median Absolute Deviation (MAD)21.3
Skewness4.871044455
Sum47936.3
Variance11958.46481
MonotonicityNot monotonic
2022-01-06T15:32:38.606114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
159
 
1.1%
8.58
 
1.0%
58
 
1.0%
288
 
1.0%
0.37
 
0.9%
17
 
0.9%
47
 
0.9%
27
 
0.9%
126
 
0.7%
1.26
 
0.7%
Other values (411)708
88.4%
(Missing)20
 
2.5%
ValueCountFrequency (%)
0.15
0.6%
0.21
 
0.1%
0.37
0.9%
0.53
0.4%
0.63
0.4%
ValueCountFrequency (%)
999.92
0.2%
9501
0.1%
9201
0.1%
8881
0.1%
7501
0.1%

generation
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.690387016
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2022-01-06T15:32:38.723512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.930419637
Coefficient of variation (CV)0.5230940897
Kurtosis-1.119009991
Mean3.690387016
Median Absolute Deviation (MAD)2
Skewness0.1172071792
Sum2956
Variance3.726519975
MonotonicityIncreasing
2022-01-06T15:32:38.826422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5156
19.5%
1151
18.9%
3135
16.9%
4107
13.4%
2100
12.5%
780
10.0%
672
9.0%
ValueCountFrequency (%)
1151
18.9%
2100
12.5%
3135
16.9%
4107
13.4%
5156
19.5%
ValueCountFrequency (%)
780
10.0%
672
9.0%
5156
19.5%
4107
13.4%
3135
16.9%

is_legendary
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
731 
1
 
70

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters801
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0731
91.3%
170
 
8.7%
2022-01-06T15:32:39.078137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
2022-01-06T15:32:39.153237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0731
91.3%
170
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0731
91.3%
170
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number801
100.0%

Most frequent character per category

ValueCountFrequency (%)
0731
91.3%
170
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common801
100.0%

Most frequent character per script

ValueCountFrequency (%)
0731
91.3%
170
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII801
100.0%

Most frequent character per block

ValueCountFrequency (%)
0731
91.3%
170
 
8.7%

Interactions

2022-01-06T15:31:13.815478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:14.021455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:14.190772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:14.361322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:14.533842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:14.715540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:14.987746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:15.177607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:15.351256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:15.530171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:15.689765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:15.847674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:16.004884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:16.404515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:16.646950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:16.832043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:17.283825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:17.503438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:17.800495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:17.961951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:18.142609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:18.331326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:18.568045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:18.770068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:18.977789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:19.177959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:19.344800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:19.564557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:19.900934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:20.068656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:20.221779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:20.374697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:20.540874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:20.713024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:20.925407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:21.094909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:21.365466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:21.662742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:21.872337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:22.078143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:22.251643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:22.469090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:22.777932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:23.066664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:23.395872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:23.555544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:23.716041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:23.864258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:24.058182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:24.218529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:24.395974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:24.599597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:24.935577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:25.227224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:25.413011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:25.598081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:25.795695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:25.984888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:26.314642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:26.624056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:26.919781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:27.088063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:27.251683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:27.416613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:27.583548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:27.746834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:27.905638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:28.078566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:28.673506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:28.890811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:29.045460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:29.271579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:30.027716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:30.208571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:30.413561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:30.595873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:30.824281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:31.017680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:31.263064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:31.457460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:31.635178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:31.825892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:32.244239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:32.450036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:32.642720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:32.820303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:32.998060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:33.191841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:33.414815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:33.617650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:33.840732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:34.020785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:34.186824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:34.540273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:35.094735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:35.543740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:35.912076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:36.659187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:37.295987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:37.936391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:38.470239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:38.967285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:39.162198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:39.470419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:39.649331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:40.034168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:40.332665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:40.552454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:40.867995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:41.250722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:41.590634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:41.906189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:42.095419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:42.296079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:42.459987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:42.678841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:42.945317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:43.224449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:43.407711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:43.599166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:44.299158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:44.850816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:45.481958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:45.723388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:45.954108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:46.147560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:46.410815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:46.563269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:46.721333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:47.304230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:47.471810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:47.640526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:47.799949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:47.957156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:48.138825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:48.313517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:48.487673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:48.650222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:48.869338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:49.128731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:49.566336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:49.879318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:50.184886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:50.437627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:50.604858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:50.751590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:50.888282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:51.065744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:51.328457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:31:51.526222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-01-06T15:32:20.828765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:20.950060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:21.071732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:21.205882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:21.327852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:21.452925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:21.592322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:21.733617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:21.856318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:21.981665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:22.268807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:22.403810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:22.543900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:22.678940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:22.815561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:22.962735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:23.092120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:23.224393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:23.367820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:23.499131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:23.693171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:23.855957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:23.994117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:24.124532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:24.256253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:24.394150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:24.530179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:24.656126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-06T15:32:24.785840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-01-06T15:32:39.456917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-06T15:32:39.867500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-06T15:32:40.260366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-06T15:32:40.654097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-01-06T15:32:41.003221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-01-06T15:32:25.169374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-06T15:32:26.284081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-06T15:32:26.570242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-06T15:32:26.760802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

abilitiesagainst_bugagainst_darkagainst_dragonagainst_electricagainst_fairyagainst_fightagainst_fireagainst_flyingagainst_ghostagainst_grassagainst_groundagainst_iceagainst_normalagainst_poisonagainst_psychicagainst_rockagainst_steelagainst_waterattackbase_egg_stepsbase_happinessbase_totalcapture_rateclassficationdefenseexperience_growthheight_mhpjapanese_namenamepercentage_malepokedex_numbersp_attacksp_defensespeedtype1type2weight_kggenerationis_legendary
0['Overgrow', 'Chlorophyll']1.001.01.00.50.50.52.02.01.00.251.02.01.01.02.01.01.00.54951207031845Seed Pokémon4910598600.745FushigidaneフシギダネBulbasaur88.11656545grasspoison6.910
1['Overgrow', 'Chlorophyll']1.001.01.00.50.50.52.02.01.00.251.02.01.01.02.01.01.00.56251207040545Seed Pokémon6310598601.060FushigisouフシギソウIvysaur88.12808060grasspoison13.010
2['Overgrow', 'Chlorophyll']1.001.01.00.50.50.52.02.01.00.251.02.01.01.02.01.01.00.510051207062545Seed Pokémon12310598602.080FushigibanaフシギバナVenusaur88.1312212080grasspoison100.010
3['Blaze', 'Solar Power']0.501.01.01.00.51.00.51.01.00.502.00.51.01.01.02.00.52.05251207030945Lizard Pokémon4310598600.639HitokageヒトカゲCharmander88.14605065fireNaN8.510
4['Blaze', 'Solar Power']0.501.01.01.00.51.00.51.01.00.502.00.51.01.01.02.00.52.06451207040545Flame Pokémon5810598601.158LizardoリザードCharmeleon88.15806580fireNaN19.010
5['Blaze', 'Solar Power']0.251.01.02.00.50.50.51.01.00.250.01.01.01.01.04.00.52.010451207063445Flame Pokémon7810598601.778LizardonリザードンCharizard88.16159115100fireflying90.510
6['Torrent', 'Rain Dish']1.001.01.02.01.01.00.51.01.02.001.00.51.01.01.01.00.50.54851207031445Tiny Turtle Pokémon6510598600.544ZenigameゼニガメSquirtle88.17506443waterNaN9.010
7['Torrent', 'Rain Dish']1.001.01.02.01.01.00.51.01.02.001.00.51.01.01.01.00.50.56351207040545Turtle Pokémon8010598601.059KameilカメールWartortle88.18658058waterNaN22.510
8['Torrent', 'Rain Dish']1.001.01.02.01.01.00.51.01.02.001.00.51.01.01.01.00.50.510351207063045Shellfish Pokémon12010598601.679KamexカメックスBlastoise88.1913511578waterNaN85.510
9['Shield Dust', 'Run Away']1.001.01.01.01.00.52.02.01.00.500.51.01.01.01.02.01.01.030384070195255Worm Pokémon3510000000.345CaterpieキャタピーCaterpie50.010202045bugNaN2.910

Last rows

abilitiesagainst_bugagainst_darkagainst_dragonagainst_electricagainst_fairyagainst_fightagainst_fireagainst_flyingagainst_ghostagainst_grassagainst_groundagainst_iceagainst_normalagainst_poisonagainst_psychicagainst_rockagainst_steelagainst_waterattackbase_egg_stepsbase_happinessbase_totalcapture_rateclassficationdefenseexperience_growthheight_mhpjapanese_namenamepercentage_malepokedex_numbersp_attacksp_defensespeedtype1type2weight_kggenerationis_legendary
791['Shadow Shield']1.004.01.01.01.00.01.01.04.01.001.01.00.00.500.51.01.01.011330720068045Moone Pokémon8912500004.0137LunalaルナアーラLunalaNaN79213710797psychicghost120.071
792['Beast Boost']0.501.01.01.00.51.00.50.51.01.004.01.00.50.252.01.02.02.05330720057045Parasite Pokémon4712500001.2109UturoidウツロイドNihilegoNaN793127131103rockpoison55.571
793['Beast Boost']0.500.51.01.02.00.52.04.01.00.500.51.01.01.002.01.01.01.013930720057025Swollen Pokémon13912500002.4107MassivoonマッシブーンBuzzwoleNaN794535379bugfighting333.671
794['Beast Boost']0.500.51.01.02.00.52.04.01.00.500.51.01.01.002.01.01.01.0137307200570255Lissome Pokémon3712500001.871PheroacheフェローチェPheromosaNaN79513737151bugfighting25.071
795['Beast Boost']1.001.01.00.51.01.01.00.51.01.002.01.01.01.001.01.00.51.08930720057030Glowing Pokémon7112500003.883DenjyumokuデンジュモクXurkitreeNaN7961737183electricNaN100.071
796['Beast Boost']0.251.00.52.00.51.02.00.51.00.250.01.00.50.000.51.00.51.010130720057025Launch Pokémon10312500009.297TekkaguyaテッカグヤCelesteelaNaN79710710161steelflying999.971
797['Beast Boost']1.001.00.50.50.52.04.01.01.00.251.01.00.50.000.50.50.50.5181307200570255Drawn Sword Pokémon13112500000.359KamiturugiカミツルギKartanaNaN7985931109grasssteel0.171
798['Beast Boost']2.000.52.00.54.02.00.51.00.50.501.02.01.01.000.01.01.00.510130720057015Junkivore Pokémon5312500005.5223AkuzikingアクジキングGuzzlordNaN799975343darkdragon888.071
799['Prism Armor']2.002.01.01.01.00.51.01.02.01.001.01.01.01.000.51.01.01.01073072006003Prism Pokémon10112500002.497NecrozmaネクロズマNecrozmaNaN8001278979psychicNaN230.071
800['Soul-Heart']0.250.50.01.00.51.02.00.51.00.502.00.50.50.000.50.51.01.0953072006003Artificial Pokémon11512500001.080MagearnaマギアナMagearnaNaN80113011565steelfairy80.571